Hello, just to report that at page 8 there is a paragraph repeated twice. This one

Before we go any further, what is a representation? At its core, it is a way to look at the data. The same data can be looked at in different ways, leading to different representations. For example, a color image can have an RGB (red-green-blue) or HSV (hue-saturation-value) encoding. Here, the words “encoding” and “representation” mean essentially the same thing and can be used interchangeably. When encoded in these two different formats, the numerical values that represent the pixels are completely different, even though they are for the same image. Different representations are useful for solving different problems. For example, in order to find all the red parts of an image, the RGB representation is more useful; but in order to find color-saturated parts of the same image, the HSV representation is more useful. This is essentially what machine learning is all about: finding an appropriate transformation that turns the old representation of the input data into a new one, one that is amenable to solving the task at hand, such as classifying an image.